Tax Shifting and Incentives for Biodiversity Conservation on Private Lands
Bibliographic record
Abstract
Abstract Conservation in human‐dominated landscapes is challenging partly due to the high costs of land acquisition. We explored a property tax mechanism to finance conservation easements or related contracts as a partial‐property acquisition strategy to meet Convention on Biological Diversity (CBD) treaty targets to conserve critically imperiled coastal Douglas fir ecosystems in Canada. To maximize cost‐efficiency, we used systematic planning tools to prioritize 198,058 parcels for biodiversity values, estimated the cost of eliminating property tax on high‐priority parcels to engage land owners in conservation, and then calculated the tax increase on nonpriority parcels necessary to maintain tax revenue. Marginal tax rate increases of 0.13, 0.21, and 0.51% on nonpriority parcels were necessary to offset the elimination of tax revenue on ∼21,000 ha of high‐priority parcels, and potentially sufficient to increase area protection from 9% to 17% to meet CBD targets given uptake rates of 100, 50, or 25%, respectively. Sensitivity analyses suggest uptake rates of 30% to 40% could allow government to achieve a 17% target with 30% of the planning area prioritized for inclusion in a property tax mechanism. Our results suggest prioritizing parcels for biodiversity value and commensurate “tax shifting” may offer an efficient route to conservation on private land.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".